The easiest introduction to data analysis for non-majors

Even non-majors can start data analysis!
Experience the 3-step process of formulating a hypothesis, analyzing data, and testing the hypothesis.

Data analysis is emerging as a key competency in various fields.

Have you been hesitating because you don't know how to code, lack confidence in statistics, or simply don't know where to start? Now, get started with data analysis with Python expert Kim Jeong-wook. He'll help you perform basic data analysis , even if you have no coding experience or statistical background .

1. Python Review and Anaconda Installation

• We've only heard about data analysis, but what exactly does it mean?
• Jupyter Notebooks make Python easier to use.
• A summary of Python grammar essential for data analysis: data types, loops, functions, etc.

2. Learn the data analysis tool Pandas.

• Pandas, the core of data preprocessing
• Essential functions that provide information about data at a glance
• Structure of the data itself vs. structure of individual data
• Converting data types to analyzable data
• Delete/add columns with only the data I want
• Various ways to extract data
• How should I handle cases where data is empty (missing values)?
• It's exactly like Excel. Pivot tables made with Pandas.

3. Practice visualization tools Matplotlip and Seaborn with Titanic survivor data.

• Visualization methods vary depending on the type of data: categorical data vs. numerical data.
• Two ways to visualize categorical data: pie and countplot.
• When you want to know the relationship between continuous and categorical data, use Swarmplot and Violinplot.
• Don't be fooled by averages. There are many different representative values.
• The most familiar graphs: Varplot and Pointplot
• I want to see multiple graphs at a glance. How to use Facet Grid.
• What is the relationship between rates and survival rates? How to determine the correlation between the two variables.

4. Digging into Seoul Grand Park Visitor Data

• Practical Data Analysis Step 1: Understanding Data Information
• Practical Data Analysis Step 2: Data Cleaning
• Changing data types, handling missing data, adding data columns, etc.
• Step 3: Analyzing Real-World Data: Developing Hypotheses About the Data
• Step 4: Analyzing Real-World Data: Validating Hypotheses Through Visualization and Statistical Inference

Instructor Introduction

Kim Jeong-wook
Graduated from Sungkyunkwan University, Department of Economics

• Former Danal Future Business Team
• Former Samsung SDS Infrastructure Business Team
• Former Miracom Solution Factory Business Team
• CEO of Soencoding
• Python/data analysis instructor at numerous universities and institutions
• Tensorflow Developer Certificate